AWS Certified Machine Learning Engineer - Associate Study Roadmap

Begin with the AWS exam guide and the four official domains. MLA-C01 validates building, operationalizing, deploying, and maintaining ML solutions and pipelines on AWS. Keep the study plan focused on ML engineering work rather than general AI awareness or full enterprise ML architecture strategy.

Start with data storage, ingestion, transformation, validation, and feature preparation. Review S3, Glue, EMR, Athena, SageMaker Feature Store, feature consistency, data quality checks, and how poor input data affects training and inference. This phase gives the rest of the lifecycle a reliable base.

Move into training jobs, tuning, evaluation metrics, model selection, model registry, versioning, Clarify, and model performance analysis. Study how training choices affect deployment and monitoring later. A model that performs well in training but cannot be monitored, versioned, or deployed safely is not a complete ML engineering solution.

Review inference patterns and workflow automation. Compare batch transform, real-time endpoints, serverless inference, asynchronous inference, and multi-model endpoints. Study SageMaker Pipelines, CI/CD, deployment guardrails, traffic shifting, rollback thinking, endpoint auto scaling, and the operational impact of deployment choices.

Finish with production concerns: Model Monitor, CloudWatch, data quality, model quality, bias drift, retraining triggers, IAM, encryption, VPC controls, least privilege, and cost optimization. Use practice questions to identify which lifecycle stage and AWS service caused each miss, then revisit the specific documentation or course note.